In this paper, we describe our method for DCASE2019 task3: Sound Event Localization and Detection (SELD). We use four CRNN SELDnet-like single output models which run in a consecutive manner to recover all possible information of occurring events. We decompose the SELD task into estimating number of active sources, estimating direction of arrival of a single source, estimating direction of arrival of the second source where the direction of the first one is known and a multi-label classification task. We use custom consecutive ensemble to predict events' onset, offset, direction of arrival and class. The proposed approach is evaluated on the TAU Spatial Sound Events 2019 - Ambisonic and it is compared with other participants' submissions.
@article{arxiv.1908.00766,
title = {Sound source detection, localization and classification using consecutive ensemble of CRNN models},
author = {Sławomir Kapka and Mateusz Lewandowski},
journal= {arXiv preprint arXiv:1908.00766},
year = {2019}
}